DeepAI AI Chat
Log In Sign Up

Human-Like Summaries from Heterogeneous and Time-Windowed Software Development Artefacts

04/28/2020
by   Mahfouth Alghamdi, et al.
The University of Adelaide
0

Automatic text summarisation has drawn considerable interest in the area of software engineering. It is challenging to summarise the activities related to a software project, (1) because of the volume and heterogeneity of involved software artefacts, and (2) because it is unclear what information a developer seeks in such a multi-document summary. We present the first framework for summarising multi-document software artefacts containing heterogeneous data within a given time frame. To produce human-like summaries, we employ a range of iterative heuristics to minimise the cosine-similarity between texts and high-dimensional feature vectors. A first study shows that users find the automatically generated summaries the most useful when they are generated using word similarity and based on the eight most relevant software artefacts.

READ FULL TEXT

page 1

page 2

page 3

page 4

05/06/2019

Toward Human-Like Summaries Generated from Heterogeneous Software Artefacts

Automatic text summarisation has drawn considerable interest in the fiel...
10/13/2020

Sensitivity of BLANC to human-scored qualities of text summaries

We explore the sensitivity of a document summary quality estimator, BLAN...
08/28/2022

Podcast Summary Assessment: A Resource for Evaluating Summary Assessment Methods

Automatic summary assessment is useful for both machine-generated and hu...
08/07/2021

Fine-tuning GPT-3 for Russian Text Summarization

Automatic summarization techniques aim to shorten and generalize informa...
08/21/2021

Towards Personalized and Human-in-the-Loop Document Summarization

The ubiquitous availability of computing devices and the widespread use ...